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Add Java and Python code for AKAZE local features matching tutorial. Fix incorrect uses of Mat.mul() in Java code.

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Uniform Lowe's ratio test in the code.
This commit is contained in:
catree
2018-07-02 14:59:00 +02:00
parent e4b51fa8ad
commit 481af5c469
13 changed files with 405 additions and 101 deletions
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samples/cpp/tutorial_code/features2D/AKAZE_match.cpp
+18 -5
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@@ -6,11 +6,12 @@
using namespace std;
using namespace cv;
const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
const float inlier_threshold = 2.5f; // Distance threshold to identify inliers with homography check
const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio
int main(int argc, char* argv[])
{
//! [load]
CommandLineParser parser(argc, argv,
"{@img1 | ../data/graf1.png | input image 1}"
"{@img2 | ../data/graf3.png | input image 2}"
@@ -21,20 +22,25 @@ int main(int argc, char* argv[])
Mat homography;
FileStorage fs(parser.get<String>("@homography"), FileStorage::READ);
fs.getFirstTopLevelNode() >> homography;
//! [load]
//! [AKAZE]
vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;
Ptr<AKAZE> akaze = AKAZE::create();
akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
//! [AKAZE]
//! [2-nn matching]
BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;
matcher.knnMatch(desc1, desc2, nn_matches, 2);
//! [2-nn matching]
vector<KeyPoint> matched1, matched2, inliers1, inliers2;
vector<DMatch> good_matches;
//! [ratio test filtering]
vector<KeyPoint> matched1, matched2;
for(size_t i = 0; i < nn_matches.size(); i++) {
DMatch first = nn_matches[i][0];
float dist1 = nn_matches[i][0].distance;
@@ -45,8 +51,12 @@ int main(int argc, char* argv[])
matched2.push_back(kpts2[first.trainIdx]);
}
}
//! [ratio test filtering]
for(unsigned i = 0; i < matched1.size(); i++) {
//! [homography check]
vector<DMatch> good_matches;
vector<KeyPoint> inliers1, inliers2;
for(size_t i = 0; i < matched1.size(); i++) {
Mat col = Mat::ones(3, 1, CV_64F);
col.at<double>(0) = matched1[i].pt.x;
col.at<double>(1) = matched1[i].pt.y;
@@ -63,12 +73,14 @@ int main(int argc, char* argv[])
good_matches.push_back(DMatch(new_i, new_i, 0));
}
}
//! [homography check]
//! [draw final matches]
Mat res;
drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
imwrite("akaze_result.png", res);
double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
double inlier_ratio = inliers1.size() / (double) matched1.size();
cout << "A-KAZE Matching Results" << endl;
cout << "*******************************" << endl;
cout << "# Keypoints 1: \t" << kpts1.size() << endl;
@@ -80,6 +92,7 @@ int main(int argc, char* argv[])
imshow("result", res);
waitKey();
//! [draw final matches]
return 0;
}
samples/cpp/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.cpp
+1 -1
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@@ -46,7 +46,7 @@ int main( int argc, char* argv[] )
std::vector<DMatch> good_matches;
for (size_t i = 0; i < knn_matches.size(); i++)
{
if (knn_matches[i].size() > 1 && knn_matches[i][0].distance / knn_matches[i][1].distance <= ratio_thresh)
if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
{
good_matches.push_back(knn_matches[i][0]);
}
samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp
+1 -1
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@@ -48,7 +48,7 @@ int main( int argc, char* argv[] )
std::vector<DMatch> good_matches;
for (size_t i = 0; i < knn_matches.size(); i++)
{
if (knn_matches[i].size() > 1 && knn_matches[i][0].distance / knn_matches[i][1].distance <= ratio_thresh)
if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
{
good_matches.push_back(knn_matches[i][0]);
}
samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java
+10 -6
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@@ -103,8 +103,9 @@ class ImageSegmentation {
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
Core.normalize(dist, dist, 0, 1., Core.NORM_MINMAX);
Mat distDisplayScaled = dist.mul(dist, 255);
Core.normalize(dist, dist, 0.0, 1.0, Core.NORM_MINMAX);
Mat distDisplayScaled = new Mat();
Core.multiply(dist, new Scalar(255), distDisplayScaled);
Mat distDisplay = new Mat();
distDisplayScaled.convertTo(distDisplay, CvType.CV_8U);
HighGui.imshow("Distance Transform Image", distDisplay);
@@ -113,14 +114,14 @@ class ImageSegmentation {
//! [peaks]
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Imgproc.threshold(dist, dist, .4, 1., Imgproc.THRESH_BINARY);
Imgproc.threshold(dist, dist, 0.4, 1.0, Imgproc.THRESH_BINARY);
// Dilate a bit the dist image
Mat kernel1 = Mat.ones(3, 3, CvType.CV_8U);
Imgproc.dilate(dist, dist, kernel1);
Mat distDisplay2 = new Mat();
dist.convertTo(distDisplay2, CvType.CV_8U);
distDisplay2 = distDisplay2.mul(distDisplay2, 255);
Core.multiply(distDisplay2, new Scalar(255), distDisplay2);
HighGui.imshow("Peaks", distDisplay2);
//! [peaks]
@@ -144,11 +145,14 @@ class ImageSegmentation {
}
// Draw the background marker
Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
Mat markersScaled = markers.mul(markers, 10000);
Mat markersScaled = new Mat();
markers.convertTo(markersScaled, CvType.CV_32F);
Core.normalize(markersScaled, markersScaled, 0.0, 255.0, Core.NORM_MINMAX);
Imgproc.circle(markersScaled, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
Mat markersDisplay = new Mat();
markersScaled.convertTo(markersDisplay, CvType.CV_8U);
HighGui.imshow("Markers", markersDisplay);
Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
//! [seeds]
//! [watershed]
samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java
+163
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@@ -0,0 +1,163 @@
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import javax.xml.parsers.DocumentBuilder;
import javax.xml.parsers.DocumentBuilderFactory;
import javax.xml.parsers.ParserConfigurationException;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.DMatch;
import org.opencv.core.KeyPoint;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Scalar;
import org.opencv.features2d.AKAZE;
import org.opencv.features2d.DescriptorMatcher;
import org.opencv.features2d.Features2d;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.w3c.dom.Document;
import org.xml.sax.SAXException;
class AKAZEMatch {
public void run(String[] args) {
//! [load]
String filename1 = args.length > 2 ? args[0] : "../data/graf1.png";
String filename2 = args.length > 2 ? args[1] : "../data/graf3.png";
String filename3 = args.length > 2 ? args[2] : "../data/H1to3p.xml";
Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
if (img1.empty() || img2.empty()) {
System.err.println("Cannot read images!");
System.exit(0);
}
File file = new File(filename3);
DocumentBuilderFactory documentBuilderFactory = DocumentBuilderFactory.newInstance();
DocumentBuilder documentBuilder;
Document document;
Mat homography = new Mat(3, 3, CvType.CV_64F);
double[] homographyData = new double[(int) (homography.total()*homography.channels())];
try {
documentBuilder = documentBuilderFactory.newDocumentBuilder();
document = documentBuilder.parse(file);
String homographyStr = document.getElementsByTagName("data").item(0).getTextContent();
String[] splited = homographyStr.split("\\s+");
int idx = 0;
for (String s : splited) {
if (!s.isEmpty()) {
homographyData[idx] = Double.parseDouble(s);
idx++;
}
}
} catch (ParserConfigurationException e) {
e.printStackTrace();
System.exit(0);
} catch (SAXException e) {
e.printStackTrace();
System.exit(0);
} catch (IOException e) {
e.printStackTrace();
System.exit(0);
}
homography.put(0, 0, homographyData);
//! [load]
//! [AKAZE]
AKAZE akaze = AKAZE.create();
MatOfKeyPoint kpts1 = new MatOfKeyPoint(), kpts2 = new MatOfKeyPoint();
Mat desc1 = new Mat(), desc2 = new Mat();
akaze.detectAndCompute(img1, new Mat(), kpts1, desc1);
akaze.detectAndCompute(img2, new Mat(), kpts2, desc2);
//! [AKAZE]
//! [2-nn matching]
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
List<MatOfDMatch> knnMatches = new ArrayList<>();
matcher.knnMatch(desc1, desc2, knnMatches, 2);
//! [2-nn matching]
//! [ratio test filtering]
float ratioThreshold = 0.8f; // Nearest neighbor matching ratio
List<KeyPoint> listOfMatched1 = new ArrayList<>();
List<KeyPoint> listOfMatched2 = new ArrayList<>();
List<KeyPoint> listOfKeypoints1 = kpts1.toList();
List<KeyPoint> listOfKeypoints2 = kpts2.toList();
for (int i = 0; i < knnMatches.size(); i++) {
DMatch[] matches = knnMatches.get(i).toArray();
float dist1 = matches[0].distance;
float dist2 = matches[1].distance;
if (dist1 < ratioThreshold * dist2) {
listOfMatched1.add(listOfKeypoints1.get(matches[0].queryIdx));
listOfMatched2.add(listOfKeypoints2.get(matches[0].trainIdx));
}
}
//! [ratio test filtering]
//! [homography check]
double inlierThreshold = 2.5; // Distance threshold to identify inliers with homography check
List<KeyPoint> listOfInliers1 = new ArrayList<>();
List<KeyPoint> listOfInliers2 = new ArrayList<>();
List<DMatch> listOfGoodMatches = new ArrayList<>();
for (int i = 0; i < listOfMatched1.size(); i++) {
Mat col = new Mat(3, 1, CvType.CV_64F);
double[] colData = new double[(int) (col.total() * col.channels())];
colData[0] = listOfMatched1.get(i).pt.x;
colData[1] = listOfMatched1.get(i).pt.y;
colData[2] = 1.0;
col.put(0, 0, colData);
Mat colRes = new Mat();
Core.gemm(homography, col, 1.0, new Mat(), 0.0, colRes);
colRes.get(0, 0, colData);
Core.multiply(colRes, new Scalar(1.0 / colData[2]), col);
col.get(0, 0, colData);
double dist = Math.sqrt(Math.pow(colData[0] - listOfMatched2.get(i).pt.x, 2) +
Math.pow(colData[1] - listOfMatched2.get(i).pt.y, 2));
if (dist < inlierThreshold) {
listOfGoodMatches.add(new DMatch(listOfInliers1.size(), listOfInliers2.size(), 0));
listOfInliers1.add(listOfMatched1.get(i));
listOfInliers2.add(listOfMatched2.get(i));
}
}
//! [homography check]
//! [draw final matches]
Mat res = new Mat();
MatOfKeyPoint inliers1 = new MatOfKeyPoint(listOfInliers1.toArray(new KeyPoint[listOfInliers1.size()]));
MatOfKeyPoint inliers2 = new MatOfKeyPoint(listOfInliers2.toArray(new KeyPoint[listOfInliers2.size()]));
MatOfDMatch goodMatches = new MatOfDMatch(listOfGoodMatches.toArray(new DMatch[listOfGoodMatches.size()]));
Features2d.drawMatches(img1, inliers1, img2, inliers2, goodMatches, res);
Imgcodecs.imwrite("akaze_result.png", res);
double inlierRatio = listOfInliers1.size() / (double) listOfMatched1.size();
System.out.println("A-KAZE Matching Results");
System.out.println("*******************************");
System.out.println("# Keypoints 1: \t" + listOfKeypoints1.size());
System.out.println("# Keypoints 2: \t" + listOfKeypoints2.size());
System.out.println("# Matches: \t" + listOfMatched1.size());
System.out.println("# Inliers: \t" + listOfInliers1.size());
System.out.println("# Inliers Ratio: \t" + inlierRatio);
HighGui.imshow("result", res);
HighGui.waitKey();
//! [draw final matches]
System.exit(0);
}
}
public class AKAZEMatchDemo {
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new AKAZEMatch().run(args);
}
}
samples/java/tutorial_code/features2D/feature_flann_matcher/SURFFLANNMatchingDemo.java
+2 -2
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@@ -42,12 +42,12 @@ class SURFFLANNMatching {
matcher.knnMatch(descriptors1, descriptors2, knnMatches, 2);
//-- Filter matches using the Lowe's ratio test
float ratio_thresh = 0.7f;
float ratioThresh = 0.7f;
List<DMatch> listOfGoodMatches = new ArrayList<>();
for (int i = 0; i < knnMatches.size(); i++) {
if (knnMatches.get(i).rows() > 1) {
DMatch[] matches = knnMatches.get(i).toArray();
if (matches[0].distance / matches[1].distance <= ratio_thresh) {
if (matches[0].distance < ratioThresh * matches[1].distance) {
listOfGoodMatches.add(matches[0]);
}
}
samples/java/tutorial_code/features2D/feature_homography/SURFFLANNMatchingHomographyDemo.java
+2 -2
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@@ -48,12 +48,12 @@ class SURFFLANNMatchingHomography {
matcher.knnMatch(descriptorsObject, descriptorsScene, knnMatches, 2);
//-- Filter matches using the Lowe's ratio test
float ratio_thresh = 0.75f;
float ratioThresh = 0.75f;
List<DMatch> listOfGoodMatches = new ArrayList<>();
for (int i = 0; i < knnMatches.size(); i++) {
if (knnMatches.get(i).rows() > 1) {
DMatch[] matches = knnMatches.get(i).toArray();
if (matches[0].distance / matches[1].distance <= ratio_thresh) {
if (matches[0].distance < ratioThresh * matches[1].distance) {
listOfGoodMatches.add(matches[0]);
}
}
samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java
+4 -3
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@@ -7,6 +7,7 @@ import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.photo.CalibrateDebevec;
import org.opencv.photo.MergeDebevec;
@@ -70,7 +71,7 @@ class HDRImaging {
//! [Tonemap HDR image]
Mat ldr = new Mat();
TonemapDurand tonemap = Photo.createTonemapDurand();
TonemapDurand tonemap = Photo.createTonemapDurand(2.2f, 4.0f, 1.0f, 2.0f, 2.0f);
tonemap.process(hdr, ldr);
//! [Tonemap HDR image]
@@ -81,8 +82,8 @@ class HDRImaging {
//! [Perform exposure fusion]
//! [Write results]
fusion = fusion.mul(fusion, 255);
ldr = ldr.mul(ldr, 255);
Core.multiply(fusion, new Scalar(255,255,255), fusion);
Core.multiply(ldr, new Scalar(255,255,255), ldr);
Imgcodecs.imwrite("fusion.png", fusion);
Imgcodecs.imwrite("ldr.png", ldr);
Imgcodecs.imwrite("hdr.hdr", hdr);
samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py
+81
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@@ -0,0 +1,81 @@
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt
## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='../data/graf1.png')
parser.add_argument('--input2', help='Path to input image 2.', default='../data/graf3.png')
parser.add_argument('--homography', help='Path to the homography matrix.', default='../data/H1to3p.xml')
args = parser.parse_args()
img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
fs = cv.FileStorage(args.homography, cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()
## [load]
## [AKAZE]
akaze = cv.AKAZE_create()
kpts1, desc1 = akaze.detectAndCompute(img1, None)
kpts2, desc2 = akaze.detectAndCompute(img2, None)
## [AKAZE]
## [2-nn matching]
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE_HAMMING)
nn_matches = matcher.knnMatch(desc1, desc2, 2)
## [2-nn matching]
## [ratio test filtering]
matched1 = []
matched2 = []
nn_match_ratio = 0.8 # Nearest neighbor matching ratio
for m, n in nn_matches:
if m.distance < nn_match_ratio * n.distance:
matched1.append(kpts1[m.queryIdx])
matched2.append(kpts2[m.trainIdx])
## [ratio test filtering]
## [homography check]
inliers1 = []
inliers2 = []
good_matches = []
inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
for i, m in enumerate(matched1):
col = np.ones((3,1), dtype=np.float64)
col[0:2,0] = m.pt
col = np.dot(homography, col)
col /= col[2,0]
dist = sqrt(pow(col[0,0] - matched2[i].pt[0], 2) +\
pow(col[1,0] - matched2[i].pt[1], 2))
if dist < inlier_threshold:
good_matches.append(cv.DMatch(len(inliers1), len(inliers2), 0))
inliers1.append(matched1[i])
inliers2.append(matched2[i])
## [homography check]
## [draw final matches]
res = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, inliers1, img2, inliers2, good_matches, res)
cv.imwrite("akaze_result.png", res)
inlier_ratio = len(inliers1) / float(len(matched1))
print('A-KAZE Matching Results')
print('*******************************')
print('# Keypoints 1: \t', len(kpts1))
print('# Keypoints 2: \t', len(kpts2))
print('# Matches: \t', len(matched1))
print('# Inliers: \t', len(inliers1))
print('# Inliers Ratio: \t', inlier_ratio)
cv.imshow('result', res)
cv.waitKey()
## [draw final matches]
samples/python/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.py
+1 -1
View File
@@ -29,7 +29,7 @@ knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)
ratio_thresh = 0.7
good_matches = []
for m,n in knn_matches:
if m.distance / n.distance <= ratio_thresh:
if m.distance < ratio_thresh * n.distance:
good_matches.append(m)
#-- Draw matches
samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py
+1 -1
View File
@@ -29,7 +29,7 @@ knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
ratio_thresh = 0.75
good_matches = []
for m,n in knn_matches:
if m.distance / n.distance <= ratio_thresh:
if m.distance < ratio_thresh * n.distance:
good_matches.append(m)
#-- Draw matches